Re: [Internet]Re: [PATCH v3] acl: support custom memory allocator

[=?gb18030?B?bWFubnl3YW5nKM3108C35Sk=?= <mannywang@tencent.com>]

Supplement: The two types of memory in ACL make a management strategy 
unnecessary: (1) the run-time memory for the match tree, which is 
allocated as a single block during each build and freed when the ACL is 
reset; and (2) the temporary memory used during the build process, which 
is allocated multiple times within a single build and then released all 
at once after the build is finished.

On 12/11/2025 9:46 AM, Stephen Hemminger wrote:
> On Tue, 25 Nov 2025 12:14:46 +0000
> "mannywang(王永峰)" <mannywang@tencent.com> wrote:
> 
>> Reduce memory fragmentation caused by dynamic memory allocations
>> by allowing users to provide custom memory allocator.
>>
>> Add new members to struct rte_acl_config to allow passing custom
>> allocator callbacks to rte_acl_build:
>>
>> - running_alloc: allocator callback for run-time internal memory
>> - running_free: free callback for run-time internal memory
>> - running_ctx: user-defined context passed to running_alloc/free
>>
>> - temp_alloc: allocator callback for temporary memory during ACL build
>> - temp_reset: reset callback for temporary allocator
>> - temp_ctx: user-defined context passed to temp_alloc/reset
>>
>> These callbacks allow users to provide their own memory pools or
>> allocators for both persistent runtime structures and temporary
>> build-time data.
>>
>> A typical approach is to pre-allocate a static memory region
>> for rte_acl_ctx, and to provide a global temporary memory manager
>> that supports multipleallocations and a single reset during ACL build.
>>
>> Since tb_mem_pool handles allocation failures using siglongjmp,
>> temp_alloc follows the same approach for failure handling.
>>
>> Signed-off-by: YongFeng Wang <mannywang@tencent.com>
>> ---
> 
> Rather than custom allocators, I did a couple of quick AI queries about
> alternatives. It looks like there are some big global gains possible
> here:
> 
> Summary of Recommendations
> Improvement	Benefit	Complexity	Priority
> ACL Object Pooling	Eliminates ACL-specific fragmentation	Medium	High
> Size-Class Segregation	Reduces general fragmentation	High	High
> Slab Allocator for Build	Better hugepage utilization	Medium	Medium
> Deferred Coalescing	Reduces fragmentation from churn	Medium	Medium
> Thread-Local Caching	Reduces contention, improves locality	Medium	Medium
> 
> Also adding some malloc_trim() would help.
> 
> https://claude.ai/share/75fcf73c-17e3-4f41-8590-f2ab640f9512
> 
>